From 855296be558b88c4e7707feac4f3923e01f37246 Mon Sep 17 00:00:00 2001
From: Colin <colin>
Date: Sun, 25 May 2025 15:33:39 +0800
Subject: [PATCH] set the channel to 80 at first layer accuracy to 94.

---
 binary/mnist.py | 34 +++++++++++++++++++++++-----------
 1 file changed, 23 insertions(+), 11 deletions(-)

diff --git a/binary/mnist.py b/binary/mnist.py
index 8f59a4a..728fb38 100644
--- a/binary/mnist.py
+++ b/binary/mnist.py
@@ -143,13 +143,14 @@ class SimpleBNN(nn.Module):
         self.w = nn.Parameter(torch.randn(3, 784))
         self.b = nn.Parameter(torch.zeros(3, 784))
 
-        self.lut2_p = LutGroup(784, 4, 2)  # pool2 => 14*14*4
-        self.lut3_p = LutGroup(14 * 14 * 2, 4, 2)  # pool2 => 7*7*4
-        self.lut4_p = LutGroup(3 * 3 * 5 * 5 * 4, 9, 2)  # conv 3 => 5*5*8
-        # self.lut4 = LutGroup(5 * 5 * 8, 8, 8)  # conv 3 => 5*5*8
-        self.lut5_p = LutGroup(3 * 3 * 3 * 3 * 8, 9, 2)  # conv 3 => 3*3*8*2
-        # self.lut5 = LutGroup(3 * 3 * 8 * 2, 16, 16)  # conv 3 => 3*3*16
-        self.lut6_p = LutGroup(3 * 3 * 16, 9, 5)  # conv 3 => 80
+        self.lut2_p = LutGroup(784, 4, 80)  # pool2 => 14*14*4
+        self.lut3_p = LutGroup(80 * 14 * 14, 4, 1)  # pool2 => 7*7*4
+        self.lut4_p = LutGroup(80 * 5 * 5 * 3 * 3, 9, 1)  # conv 3 => 5*5*8
+        # self.lut4 = LutGroup(8 * 5 * 5, 8, 8)  # conv 3 => 5*5*8
+        self.lut5_p = LutGroup(80 * 3 * 3 * 3 * 3, 9, 1)  # conv 3 => 3*3*16
+        # self.lut5 = LutGroup(16 * 3 * 3, 16, 10)  # conv 3 => 3*3*16
+        self.lut6_p = LutGroup(80 * 3 * 3, 9, 1)  # conv 3 => 128
+        # self.lut7_p = LutGroup(8 * 16, 16, 10)  # fc 128 => 80
 
         self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
         self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
@@ -205,28 +206,39 @@ class SimpleBNN(nn.Module):
         x = x.view(batch, -1)
         x = self.lut2_p(x)
 
-        x = x.view(batch, 2, 14, 14)
+        x = x.view(batch, 80, 14, 14)
         x = F.unfold(x, kernel_size=2, dilation=1, padding=0, stride=2)
         x = x.view(batch, -1)
         x = self.lut3_p(x)
 
-        x = x.view(batch, 4, 7, 7)
+        x = x.view(batch, 80, 7, 7)
         x = F.unfold(x, kernel_size=3, dilation=1, padding=0, stride=1)
         x = x.view(batch, -1)
         x = self.lut4_p(x)
+
+        # x = x.view(batch, 8, 5, 5)
+        # x = x.permute(0, 2, 3, 1)
+        # x = x.reshape(batch, -1)
         # x = self.lut4(x)
 
-        x = x.view(batch, 8, 5, 5)
+        x = x.view(batch, 80, 5, 5)
         x = F.unfold(x, kernel_size=3, dilation=1, padding=0, stride=1)
         x = x.view(batch, -1)
         x = self.lut5_p(x)
+
+        # x = x.view(batch, 16, 3, 3)
+        # x = x.permute(0, 2, 3, 1)
+        # x = x.reshape(batch, -1)
         # x = self.lut5(x)
 
-        x = x.view(batch, 16, 3, 3)
+        x = x.view(batch, 80, 3, 3)
         x = F.unfold(x, kernel_size=3, dilation=1, padding=0, stride=1)
         x = x.view(batch, -1)
         x = self.lut6_p(x)
 
+        # x = x.view(batch, 8, 16)  # 16 channel   8 value/channel
+        # x = self.lut7_p(x)  # 10 * 8 value/channel
+
         # xx = 2 ** torch.arange(7, -1, -1).to(x.device)
         x = x.view(batch, -1, 8)
         # x = x * xx